Reactive rearrangement of parts under sensor inaccuracy: particle filter approach

The paper addresses the warehouseman´s problem with geometrical simplifications under the more realistic case of imperfect sensory information. In this scenario, a 2D workspace contains an actuated robot and a set of unactuated parts. The discrepancy between the robot´s and/or the parts´ real and measured positions may lead to jerky movements or even collisions in the parts´ moving problem we are concerned with. Thus, we need to approximate the state information - taking the highly nonlinear nature of the resulting system into account. This is accomplished using particle filters - which implement recursive Bayesian filter in nonlinear and/or nongaussian environments. For the model of parts which turns out to be linear, the approach reduces to Kalman filtering. First the robot´s dynamic model and the measurement model are modified to incorporate the inaccuracies in the sensory data; and then the particle filter is utilized to get improved positional estimate. Enhancements in the robot´s movements and reduction in the number of collisions have been verified through extensive computer simulations